EconPapers    
Economics at your fingertips  
 

Energy cost and consumption reduction of an office building by Chaotic Satin Bowerbird Optimization Algorithm with model predictive control and artificial neural network: A case study

Xiao Chen, Benyi Cao and Somayeh Pouramini

Energy, 2023, vol. 270, issue C

Abstract: A large amount of energy consumed globally is done by buildings, also, buildings are responsible for a great portion of greenhouse gas emissions. With progress in smart sensors and devices, a new generation of smarter and more context-aware building controllers can be developed. Consequently, zone-level surrogate artificial neural networks are used herein, where indoor temperature, occupancy, and weather data are the inputs. A new metaheuristic optimization algorithm, called Chaotic Satin Bowerbird Optimization Algorithm (CSBOA) is employed for the minimization of energy consumption. 24-hour schedules of the heating setpoint of each zone are created for an office building located in Edinburgh, Scotland. Two modes of optimization including day-ahead and model predictive control are applied for each hour. The consumption of energy decreased by 26% during a test week in Feb in comparison to the base case approach of heating. By definition of a time-of-use tariff, the cost of energy consumption is decreased by around 28%.

Keywords: Energy consumption reduction; Cost reduction; Chaotic satin Bowerbird optimization algorithm; Artificial neural network; Model predictive control; Setpoint schedule of heating; HVAC control (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations: View citations in EconPapers (8)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544223002682
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:270:y:2023:i:c:s0360544223002682

DOI: 10.1016/j.energy.2023.126874

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu (repec@elsevier.com).

 
Page updated 2024-12-28
Handle: RePEc:eee:energy:v:270:y:2023:i:c:s0360544223002682